
We introduce the first transformer-based approach to address the challenging task of egocentric gaze estimation.


We utilize a global visual token embedding strategy to incorporate global visual context into self-attention, and further introduce a novel Global-Local Correlation module to explicitly model the correlation between global context and each local visual token.


Our novel design obtains consistent improvement on the EGTEA Gaze+ [1]} and Ego4D [2]} datasets and outperforms previous state-of-the-art methods by at least 3.9% on EGTEA and 5.6% on Ego4D in F1 score. Importantly, this is the first work that uses the Ego4D dataset for egocentric gaze estimation, which serves as important benchmark for future research in this direction.

